There are however several examples where the interactions among individuals, being them neurons [1]}, [2]}, proteins [3]}, animals [4]}, [5]} or authors of scientific papers [6]}, [7]}, cannot be reduced to binary
interactions. The group action is indeed the real driver of the dynamics. Starting from this observation, higher-order models have been developed so as to capture the many body interactions among individual units. We hereby focus on hypergraphs [8]}, [9]}, [10]}, versatile tools with a broad potential that is still being fully elucidated. Hypergraphs have been applied to different fields from social contagion model [11]}, [12]}, to the modelling of random walks [7]}, from the study of synchronisation [14]}, [15]}, [16]} and diffusion [12]}, to non-linear consensus [18]}, via the emergence of Turing patterns [16]}. It is also worth mentioning an alternative approach to high-order interactions which exploits the notion of simplicial complexes [20]}, [21]}, [22]}. Largely used in the past to tackle optimisation or algebraic problems, they have been recently invoked to address problems in epidemic spreading [23]}, [24]} or synchronisation phenomena [25]}, [26]}, [27]}. In this work we will however adopt the viewpoint of
hypergraphs, to represent high-order interactions.
